-
Notifications
You must be signed in to change notification settings - Fork 91
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
problems with package dependencies #30
Comments
You should be able to find the SpatialTransformer in neurite's legacy branch, or in the voxelmorph package. pystrum should have most of the same functionality as pytools-lib; is there a particular function that is missing? |
I found everything, and I made it work, so thank you. I also wanted to ask it is possible to implement the paper using voxelmorph directly (i.e. using VxmDense instead of TransformModelTrainer.) I am in general a bit confused with all the voxelmorph ecosystem. |
@gonlairo |
Great! Absolutely, if you want to implement the training code yourself using voxelmorph, that is possible. Most of the code in this repo deals with the application of random spatial and appearance transformations for model training and inference, and it uses voxelmorph for the spatial transformations. |
Thank you! I am trying to implement the appearance (intensity) model using voxelmorph and in the paper, you state that "[to learn the appearance model] we use an image similarity loss as well as a semantically-aware smoothness regularization.". Is it possible then to train an appearance model with voxelmorph using just SpatialSegmentSmoothness loss instead of MSE or NCC? Any tips would be appreciated :) |
@gonlairo Have you been able to implement using voxelmorph? Thank you! |
I would like to use your method to perform data augmentation on my dataset but I am running into a lot of errors due to package dependencies e.g pytools-lib is now called pystrum or the new version of neurite does not have the SpatialTransformer layer. What is the best approach to run your code?
The text was updated successfully, but these errors were encountered: